hgpu.org » GeForce RTX 2080 Ti
NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics
Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis
Tags: Biology, Chemistry, CUDA, GeForce RTX 2080 Ti, Machine learning, Molecular dynamics, Molecular simulation, Neural networks, nVidia, Package
January 23, 2022 by hgpu
Recent source codes
* * *
Most viewed papers (last 30 days)
- CuPBoP-AMD: Extending CUDA to AMD Platforms
- A Review of the Parallelization Strategies for Iterative Algorithms
- GT4Py: High Performance Stencils for Weather and Climate Applications using Python
- RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs
- Accelerating bioinformatics applications on CUDA-enabled multi-GPU systems
- Solving MaxSAT with Matrix Multiplication
- Testing and Mutation Testing for GPU Kernels
- Evaluation of FPGA-based high performance computing platforms
- On the Three P's of Parallel Programming for Heterogeneous Computing: Performance, Productivity, and Portability
- CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types
* * *